Even the most experienced R users need help creating elegant graphics. The ggplot2 library is a phenomenal tool for creating graphics in R but even after many years of near-daily use we still need to refer to our Cheat Sheet. But for our own benefit and hopefully yours we decided to post the most useful bits of code. Under the hood of ggplot2 graphics in R Mapping in R using the ggplot2 package A new data processing workflow for R: dplyr, magrittr, tidyr and ggplot2. In ggplot2 versions before 2. With 2. The margin argument uses the margin function and you provide the top, right, bottom and left margins the default unit is points. Note that you can also use different fonts. This may not work on a Mac send me a note and let me know. If you are having trouble with this you might take a look at this StackOverflow discussion. You can use the lineheight argument to change the spacing between lines. The former removes all data points outside the range and second adjusts the visible area. There must be a better way than this. You can use a function in this case. Here is an example:. We will color code the plot based on season. You can see that by default the legend title is what we specified in the color argument. To change the title of the legend you would use the name argument in your scale function. I have mixed feelings about those boxes. Points in the legend get a little lost, especially without the boxes. To override the default try:. By default, both the points and the label text end up in the legend like this again, who would make a plot like this? Here is the default:. We are mapping the lines and the points using aes and we are mapping not to a variable in our dataset but to a single string so that we get just one color for each. I wanted grey and red. Tantalizingly close! The final step is to override the aesthetics in the legend. The guide function allows us to control guides like the legend:.
Visualizing categorical data in r
R in Action 2nd ed significantly expands upon this material. The ggplot2 package, created by Hadley Wickham, offers a powerful graphics language for creating elegant and complex plots. Its popularity in the R community has exploded in recent years. Origianlly based on Leland Wilkinson's The Grammar of Graphicsggplot2 allows you to create graphs that represent both univariate and multivariate numerical and categorical data in a straightforward manner. Grouping can be represented by color, symbol, size, and transparency. The creation of trellis plots i. Mastering the ggplot2 language can be challenging see the Going Further section below for helpful resources. There is a helper function called qplot for quick plot that can hide much of this complexity when creating standard graphs. The qplot function can be used to create the most common graph types. While it does not expose ggplot 's full power, it can create a very wide range of useful plots. The format is:. Here are some examples using automotive data car mileage, weight, number of gears, number of cylinders, etc. Unlike base R graphs, the ggplot2 graphs are not effected by many of the options set in the par function. They can be modified using the theme function, and by adding graphic parameters within the qplot function. For greater control, use ggplot and other functions provided by the package. We have only scratched the surface here. To learn more, see the ggplot reference siteand Winston Chang's excellent Cookbook for R site. Though slightly out of date, ggplot2: Elegant Graphics for Data Anaysis is still the definative book on this subject. Try the free first chapter of this interactive tutorial on ggplot2. Kabacoff, Ph. Graphics with ggplot2 The ggplot2 package, created by Hadley Wickham, offers a powerful graphics language for creating elegant and complex plots. For line plots, color associates levels of a variable with line color. For density and box plots, fill associates fill colors with a variable. Legends are drawn automatically. The geom option is expressed as a character vector with one or more entries. When the number of observations is greater than 1, a more efficient smoothing algorithm is employed. Methods include "lm" for regression, "gam" for generalized additive models, and "rlm" for robust regression. The formula parameter gives the form of the fit. Note that the formula uses the letters x and y, not the names of the variables. For univariate plots for example, histogramsomit y xlab, ylab Character vectors specifying horizontal and vertical axis labels xlim,ylim Two-element numeric vectors giving the minimum and maximum values for the horizontal and vertical axes, respectively Notes: At present, ggplot2 cannot be used to create 3D graphs or mosaic plots.
Ggplot in r
Plot two categorical variables in r
This R tutorial describes how to create a barplot using R software and ggplot2 package. Data derived from ToothGrowth data sets are used. In this case, the height of the bar represents the count of cases in each category. Barplot outline colors can be automatically controlled by the levels of the variable dose :. Read more on ggplot2 colors here : ggplot2 colors. In the R code below, barplot fill colors are automatically controlled by the levels of dose :. The allowed values for the arguments legend. Read more on ggplot legend : ggplot2 legend. ToothGrowth describes the effect of Vitamin C on tooth growth in Guinea pigs. Three dose levels of Vitamin C 0. A stacked barplot is created by default. The barplot fill color is controlled by the levels of dose :. If you want to place the labels at the middle of bars, you have to modify the cumulative sum as follow :. If the variable on x-axis is numeric, it can be useful to treat it as a continuous or a factor variable depending on what you want to do :. The helper function below will be used to calculate the mean and the standard deviation, for the variable of interest, in each group :. This analysis has been performed using R software ver. Basic barplots Data Create barplots Bar plot with labels Barplot of counts Change barplot colors by groups Change outline colors Change fill colors Change the legend position Change the order of items in the legend Barplot with multiple groups Data Create barplots Add labels Barplot with a numeric x-axis Barplot with error bars Customized barplots Infos. Basic barplots Data Data derived from ToothGrowth data sets are used. To make a barplot of counts, we will use the mtcars data sets : head mtcars mpg cyl disp hp drat wt qsec vs am gear carb Mazda RX4 Change the legend position Change bar fill colors to blues p The allowed values for the arguments legend. Barplot with multiple groups Data Data derived from ToothGrowth data sets are used. Create barplots A stacked barplot is created by default. Barplot with a numeric x-axis If the variable on x-axis is numeric, it can be useful to treat it as a continuous or a factor variable depending on what you want to do : Create some data df2 supp dose len 1 VC 0. Infos This analysis has been performed using R software ver. Enjoyed this article? Show me some love with the like buttons below Thank you and please don't forget to share and comment below!! Montrez-moi un peu d'amour avec les like ci-dessous Recommended for You! Practical Guide to Cluster Analysis in R. Network Analysis and Visualization in R. More books on R and data science.
Plot categorical data r ggplot
Plot multiple categorical variables in r
These categorical columns are called Factors in R. Looking at the diamonds data set we can see how this is set up in R. Here we can see the cutcolor and clarity columns are all non-numeric, textual data. These are the factor variables of this dataset. We can confirm that by asking for the class of the column, that is, the type of data in it. The spots are all overlapping, we can force the different colours to stay separate with the position option. The width option tells the spots how far to stay apart. And of course, the whole thing still works even if we are comparing two numerical columns. We can still use the aesthetic mapping in the geom to colour our points by a factor. Instead small multiple plots different data, same settings can be used. We use the factors to define the facet. The built in dataset CO2 describes measurement of CO2 uptake versus concentration for Quebec and Mississippi grasses in chilled and nonchilled tests. The dataset is as follows:. Work hard to be lazy. Using ggplot2 for producing plots. The dataset is as follows: Type is a factor column with two levels Quebec and Mississippi Treatment is a factor colum with two levels nonchilled and chilled Uptake is a numerical colum with CO2 uptake rate in micromoles per metre squared per second Plant is a factor with twelve levels, one for each individual plant assayed.
If your data needs to be restructured, see this page for more information. Here is some sample data derived from the tips dataset in the reshape2 package :. In these examples, the height of the bar will represent the value in a column of the data frame. In these examples, the height of the bar will represent the count of cases. For line graphs, the data points must be grouped so that it knows which points to connect. When more variables are used and multiple lines are drawn, the grouping for lines is usually done by variable this is seen in later examples. This is derived from the tips dataset in the reshape2 package. To draw multiple lines, the points must be grouped by a variable; otherwise all points will be connected by a single line. In this case, we want them to be grouped by sex. The issue is explained here. When the variable on the x-axis is numeric, it is sometimes useful to treat it as continuous, and sometimes useful to treat it as categorical. In this data set, the dose is a numeric variable with values 0. It might be useful to treat these values as equal categories when making a graph. This is derived from the ToothGrowth dataset included with R. A simple graph might put dose on the x-axis as a numeric value. It is possible to make a line graph this way, but not a bar graph. If you wish to treat it as a categorical variable instead of a numeric one, it must be converted to a factor. This can be done by modifying the data frame, or by changing the specification of the graph. It is also possible to make a bar graph when the variable is treated as categorical rather than numeric. Problem Solution Basic graphs with discrete x-axis Bar graphs of values Bar graphs of counts Line graphs Graphs with more variables Bar graphs Line graphs Finished examples With a numeric x-axis With x-axis treated as continuous With x-axis treated as categorical Problem You want to do make basic bar or line graphs. Basic graphs with discrete x-axis With bar graphs, there are two different things that the heights of bars commonly represent: The count of cases for each group — typically, each x value represents one group. The value of a column in the data set.
Ggplot two discrete variables
They are two types of users that are the classifiers in this dataset:. By the end, I will show you how to improve your ggplot graphs by learning new functions and arguments to best visualize the data, including:. First we will want to perpetually mutate our date and time numerics into categorical ranges that better represent the data. I want to classify intervals of the day into time periods morning, noon, etc. We can do this by extracting the date in hours, then cutting the hours into time intervals that best represent these periods. The structure of the duration is in seconds and will be changed to a metric that is easier to digest, like minutes. This is a better graph, but the usage difference between customers and subscribers is hard to see. This reveals more perspective on the difference in volume between subscribers and customers, especially on weekdays. On weekends, the users both have similar habits. However, the volume is much lower because it seems most use Ford GoBikes to commute during the weekdays. Unsurprisingly, a majority of weekday users appear to be subscribers commuting to and from work. This can also be shown when we investigate the riding intervals:. Most users ride between 10 and 25 minutes on weekends. Usage over 25 minutes is mainly by customers instead of subscribers. Before, we were looking at the dataset in the span of a day. The first problem here is that the scale on the y-axis poorly visualizes the data in months with low volume. In this practice, we learned to manipulate dates and times and used ggplot to explore our dataset. We even deduced a few things about the behaviours of our customers and subscribers. How you visualize the data is very fascinating. Thanks for sharing your project with us along with tips! Like Like. You are commenting using your WordPress. You are commenting using your Google account. You are commenting using your Twitter account. You are commenting using your Facebook account.
Ggplot cheat sheet
Facebook LinkedIn Twitter. Blog Courses QBits Consulting. Quiz: Scatter Plot Facts Which of the following statements about scatter plots are correct? Scatter plots visualize the relation of categorical and numeric variables. In a scatter plot we only interpret single points and never the relationship between the variables in general. Table of Contents Create a scatter plot with ggplot Create a scatter plot with ggplot. Why data visualization is important. Sign in to continue Sign up and unlock free chapters, quizzes and code exercises! First name Last name Optional. Alternative logins Facebook Google. Quiz: Scatter Plot Facts. Scatter plots visualize the relation of two numeric variables Scatter plots use points to visualize observations. Save Progress Not Now. Introduction to data visualization. Quiz: Visualization Phase. Available Plot Types. Quiz: Distribution Comparison Plots. Introducing: GGPlot2. Quiz: GGPlot2 Facts. Create a scatter plot with ggplot. Introduction to scatter plots. Specifying a dataset. Exercise: Specify the gapminder dataset. Specifying a geometric layer. Quiz: Scatter Plot Layers. Creating aesthetic mappings. Exercise: Visualize the Gapminder dataset. Specify additional aesthetics for points. Adding more plot aesthetics. Adjusting point color. Exercise: Reconstruct Gapminder graph. Exercise: Create a colored scatter plot with DavisClean. Adjusting point size.
Intro to Data Visualization with R & ggplot2